tigerneil/awesome-deep-rl
An extensive curated collection of deep reinforcement learning papers, benchmarks, and frameworks spanning value-based methods, policy gradients, multi-agent systems, and AGI research.
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This repository compiles important contributions in deep reinforcement learning as an organized awesome list. It covers topics including value-based methods, policy gradients, model-based RL, multi-agent systems, hierarchical RL, meta-learning, and connections to AGI. The list serves as a reference for researchers and practitioners studying deep RL fundamentals and recent advances.
Frequently asked
- What is tigerneil/awesome-deep-rl?
- An extensive curated collection of deep reinforcement learning papers, benchmarks, and frameworks spanning value-based methods, policy gradients, multi-agent systems, and AGI research.
- Is awesome-deep-rl open source?
- Yes — tigerneil/awesome-deep-rl is open source, released under the MIT license.
- What language is awesome-deep-rl written in?
- tigerneil/awesome-deep-rl is primarily written in HTML.
- How popular is awesome-deep-rl?
- tigerneil/awesome-deep-rl has 1.5k stars on GitHub.
- Where can I find awesome-deep-rl?
- tigerneil/awesome-deep-rl is on GitHub at https://github.com/tigerneil/awesome-deep-rl.